nbdev

e, written in Rust. - https://github.com/EddieHubCommunity/LinkFree: Connecting with your audience with a single link, showcasing your content and projects. - https://github.com/FashionFreedom/Seamly2D: Design CAD to democratize and de-centralize fashion design & production. - https://github.com/fastai/nbdev: Increase developer productivity by 10x with a new exploratory programming workflow. - https://github.com/formbricks/formbricks: Open-source Qualtrics alternative - https://github.com/GyulyVGC/sniffnet: Cross-platform application to comfortably monitor and analyse network traffic - https://github.com/JessicaTegner/pypandoc: Pypandoc provides a thin wrapper for pandoc, a universal document converter. - https://github.com/mockoon/mockoon: Mockoon is the easiest and quickest way to design and run mock REST APIs. - https://github.com/nuxt/nuxt: An intuitive framework for building web applications, built for the edge. - https://github.com/responsively-org/responsively-app: A dev-tool for web developers that aid in faster responsive web page development. - https://github.com/simonw/datasette: An open source multi-tool for exploring and publishing data - https://github.com/spyder-ide/spyder: The scientific Python development environment - https://github.com/strawberry-graphql/strawberry: A GraphQL library for Python that leverages type annotations - https://github.com/termux/termux-app: A terminal emulator for Android that provides 2000+ linux packages to code on phone - https://github.com/TimothyStiles/poly: Poly is a fast, well tested Go package for engineering organisms. - https://github.com/trpc/trpc: End-to-end typesafe APIs made easy. Never write another API contract again. Edit: formatting and updated repo links

ther approach; though it's not Sphinx: > nbdev is a library that allows you to fully develop a library in Jupyter Notebooks, putting all your code, tests and documentation in one place. > [...] Add %nbdev_export flags to the cells that define the functions you want to include in your python modules https://github.com/fastai/nbdev A few additional sources of docs for Sphinx and ReStructuredText: Read The Docs docs > Getting Started with Sphinx > External Resources https://docs.readthedocs.io/en/stable/intro/getting-started-... CPython Devguide > "Documenting Python" https://devguide.python.org/documenting/ "How to write [Linux] kernel documentation" https://www.kernel.org/doc/html/latest/doc-guide/index.html awesome-sphinxdoc: https://github.com/yoloseem/awesome-sphinxdoc ... "Ask HN: Recommendations for Books on Writing [for engineers]?" https://news.ycombinator.com/item?id=23945580

ion.h... REES configuration files: https://repo2docker.readthedocs.io/en/latest/config_files.ht... Storing a container built with repo2docker in a container registry is one way to increase the likelihood that it'll be possible to run the same analysis pipeline with the same data and get the same results years later. ... Pachyderm ( https://pachyderm.io/platform/ ) does Data Versioning, Data Pipelines (with commands that each run in a container), and Data Lineage (~ "data provenance"). What other platforms are there for versioning data and recording data provenance? ... Recording manual procedures is an area where we've somewhat departed from the "write in a lab notebook with a pen" practice. CoCalc records all (collaborative) inputs to the notebook with a timeslider for review. In practice, people use notebooks for displaying generated charts, manual exploratory analyses (which does introduce bias), for demonstrating APIs, and for teaching. Is JupyterLab an ideal IDE? Nope, not by a longshot. nbdev makes it easier to write a function in a notebook, sync it to a module, edit it with a more complete data-science IDE (like RStudio, VSCode, Spyder, etc), and then copy it back into the notebook. https://github.com/fastai/nbdev

ing and preparing data, then trying to model the question at hand, and finally presenting results. The model is generally averse to ever scrolling back up. This doesn't feel at all like REPL-based development, where you're experimenting on one small part of a wider web of code, but the REPL history itself is much more transient and throwaway. There certainly can be a core loop in analysis where you're inspecting data, learning the patterns and distributions etc, but that generally yields insights, not code. I assume that this isn't universally true, and many use R/Python REPLs to support more of a code-first approach, but I haven't observed that to be common. 0: https://github.com/fastai/nbdev